Once the bipartite graphs are created, our next goal is to identify the neighbors of pin nodes from the bipartite graphs. As mentioned earlier, the "on-the-fly" sampling of neighbors during model training from billion-nodes and billion-edges graph is extremely inefficient and not practical. Hence, we identify the node neighbors before training MultiBiSage using Pixie [1]} system. Pixie system loads the entire graph in 2 TB RAM machine and performs random-walks using optimized C++ threads. For each pin node, we select top-k highly visited pin neighbors. The pseudo-code of Pixie's random-walker is shared in the Appendix . Table REF  shows a sample of the top-5 highly visited pins of a candidate pin.
